pip install linkup-sdk            # Python
# or
npm install linkup-sdk            # TypeScript

export LINKUP_API_KEY="your-api-key"   # treat like a password

from linkup import LinkupClient

client = LinkupClient(api_key="<YOUR_LINKUP_API_KEY>")

# depth: "fast" | "standard" | "deep"
# - fast: sub-second, keyword-only, no LLM, no scraping, no chaining (beta)
# - standard: single-iteration agentic, ~1-3s, can scrape ONE URL provided in the query, parallel sub-searches OK
# - deep: up to 10 iterations, ~5-30s, can scrape multiple URLs, supports sequential search->scrape chains
# Use `deep` when the next step depends on the previous step's output.
#
# outputType:
# - searchResults: array of {name, url, content} — best for LLM grounding
# - sourcedAnswer: natural language + sources — best for end-user display
# - structured: JSON matching structuredOutputSchema — best for pipelines
response = client.search(
    query="What is Microsoft's 2024 revenue?",
    depth="standard",
    output_type="sourcedAnswer",
)
print(response)

{
  "type": "function",
  "function": {
    "name": "linkup_search",
    "description": "Searches the live web with an agentic search engine. Returns ranked sources, a sourced natural-language answer, or structured JSON. Use whenever the model needs current facts, specific named entities, recent events, or information that likely isn't in training data.",
    "parameters": {
      "type": "object",
      "properties": {
        "query": {
          "type": "string",
          "description": "Natural-language question or instruction. Be specific. Include the key entity, date range, or domain when known. For chained work (search then scrape), specify the order: 'First find X. Then scrape X. Then extract Y.'"
        },
        "depth": {
          "type": "string",
          "enum": ["fast", "standard", "deep"],
          "description": "fast: sub-second keyword search, no LLM, no scraping. standard: single-iteration agentic, ~1-3s, can scrape one URL given in the query. deep: up to 10 iterations, ~5-30s, can scrape multiple URLs, supports sequential search-then-scrape chains. Use deep when the next step depends on the previous step's output."
        },
        "outputType": {
          "type": "string",
          "enum": ["searchResults", "sourcedAnswer", "structured"],
          "description": "searchResults for LLM grounding. sourcedAnswer for end-user display. structured for pipelines (requires structuredOutputSchema)."
        },
        "structuredOutputSchema": {
          "type": "string",
          "description": "Required when outputType=structured. A JSON Schema (as a string) the response must match. Root must be type 'object'. Keep schemas shallow."
        },
        "includeDomains": {
          "type": "array",
          "items": { "type": "string" },
          "description": "Restrict the search to up to 100 domains. Recommended when trusted sources for the use case are known."
        },
        "excludeDomains": {
          "type": "array",
          "items": { "type": "string" },
          "description": "Domains to exclude from results."
        },
        "fromDate": {
          "type": "string",
          "description": "ISO 8601 (YYYY-MM-DD). Restrict to results on or after this date. Prefer this over embedding dates in the query."
        },
        "toDate": {
          "type": "string",
          "description": "ISO 8601 (YYYY-MM-DD). Restrict to results on or before this date."
        }
      },
      "required": ["query", "depth", "outputType"]
    }
  }
}